Combined linear regression adaptation and Bayesian predictive classification for robust speech recognition

The uncertainty in parameter estimation due to the adverse environments deteriorates the speech recognition performance. It becomes crucial to incorporate the parameter uncertainty into decision so that the classification robustness can be assured. In this paper, we propose a linear regression based Bayesian predictive classification (LRBPC) for robust speech recognition. This framework is constructed under the paradigm of linear regression adaptation of HMM’s. Because the regression mapping between HMM’s and adaptation data is ill posed, we properly characterize the uncertainty of regression parameters using a joint Gaussian distribution. A predictive distribution is derived to set up the LRBPC decision. Such decision is robust compared to the plug-in maximum a posteriori decision adopted in the maximum likelihood linear regression (MLLR). Since the specified distribution belongs to the conjugate prior family, the evolutionary hyperparameter is established. With the hyperparameter, the LRBPC achieves significantly better performance than MLLR adaptation in car speech recognition.